The present disclosure relates generally to a method for securing patient identity and in particular, to a method for de-identifying patient data at an ambulatory patient care provider (PCP) site for submission to a data warehouse system and then re-identifying a patient, at the PCP site, from de-identified patient data received from the data warehouse system.
Data warehousing methods have been used to aggregate, clean, stage, report and analyze patient information derived from medical claims billing and electronic medical records (EMR). Patient data may be extracted from multiple EMR databases located at PCP sites in geographically dispersed locations, then transported and stored in a centrally located data warehouse. The central data warehouse may be a source of information for population-based profile reports of physician productivity, preventative care, disease-management statistics and research on cinical outcomes. Patient data is sensitive and confidential, and therefore, specific identifying information must be removed prior to transporting it from a PCP site to a central data warehouse. This removal of identifying information must be performed per the federal Health Insurance Portability and Accountability Act (HIPAA) regulations. Any data that is contained in a public database must not reveal the identity of the individual patients whose medical information is contained in the database. Because of this requirement, any information contained on a medical report or record that could aid in tracing back to a particular individual must be removed from the report or record prior to adding the data to a data warehouse for public data mining.
In order to accurately assess the impact of a particular drug or treatment on a patient it is helpful to analyze all medical reports relating to the particular patient. Removing data that can be used to trace back to an individual patient can make it impossible to group and analyze all medical reports relating to a particular patient. In addition, one of the aims of population analysis is to assemble an at-risk cohort population comprised of individuals who may be candidates for clinical intervention. However, de-identified data is not very useful to the patient care providers who need to know the identity of their own patients in order to treat them.